Overview

Dataset statistics

Number of variables4
Number of observations169
Missing cells450
Missing cells (%)66.6%
Duplicate rows1
Duplicate rows (%)0.6%
Total size in memory5.4 KiB
Average record size in memory32.8 B

Variable types

Text3
Categorical1

Dataset

Description보건의료 지역보건 업무 관련 제주특별자치도 제주시 관내 병원 현황 데이터를 제공합니다.
Author제주특별자치도 제주시
URLhttps://www.data.go.kr/data/15056079/fileData.do

Alerts

Dataset has 1 (0.6%) duplicate rowsDuplicates
의료기관명 has 150 (88.8%) missing valuesMissing
주소 has 150 (88.8%) missing valuesMissing
전화번호 has 150 (88.8%) missing valuesMissing

Reproduction

Analysis started2023-12-12 04:01:47.396329
Analysis finished2023-12-12 04:01:47.879107
Duration0.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

의료기관명
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing150
Missing (%)88.8%
Memory size1.4 KiB
2023-12-12T13:01:48.017687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.7894737
Min length4

Characters and Unicode

Total characters186
Distinct characters54
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row제주선한병원
2nd row제주우리병원
3rd row탑동병원
4th row제주특별자치도제주의료원
5th row지오요양병원
ValueCountFrequency (%)
의료법인 3
 
10.7%
제주우리병원 1
 
3.6%
탑동병원 1
 
3.6%
의료법인평촌의료재단 1
 
3.6%
제주대학교병원 1
 
3.6%
한국병원 1
 
3.6%
혜인의료재단 1
 
3.6%
제주한라병원 1
 
3.6%
한마음병원 1
 
3.6%
중앙병원 1
 
3.6%
Other values (16) 16
57.1%
2023-12-12T13:01:48.387181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
원 21
 
11.3%
병 18
 
9.7%
제 12
 
6.5%
주 11
 
5.9%
의 10
 
5.4%
료 10
 
5.4%
9
 
4.8%
양 9
 
4.8%
요 9
 
4.8%
인 7
 
3.8%
Other values (44) 70
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 177
95.2%
Space Separator 9
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 21
 
11.9%
병 18
 
10.2%
제 12
 
6.8%
주 11
 
6.2%
의 10
 
5.6%
료 10
 
5.6%
양 9
 
5.1%
요 9
 
5.1%
인 7
 
4.0%
재 5
 
2.8%
Other values (43) 65
36.7%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 177
95.2%
Common 9
 
4.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 21
 
11.9%
병 18
 
10.2%
제 12
 
6.8%
주 11
 
6.2%
의 10
 
5.6%
료 10
 
5.6%
양 9
 
5.1%
요 9
 
5.1%
인 7
 
4.0%
재 5
 
2.8%
Other values (43) 65
36.7%
Common
ValueCountFrequency (%)
9
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 177
95.2%
ASCII 9
 
4.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
원 21
 
11.9%
병 18
 
10.2%
제 12
 
6.8%
주 11
 
6.2%
의 10
 
5.6%
료 10
 
5.6%
양 9
 
5.1%
요 9
 
5.1%
인 7
 
4.0%
재 5
 
2.8%
Other values (43) 65
36.7%
ASCII
ValueCountFrequency (%)
9
100.0%

주소
Text

MISSING 

Distinct18
Distinct (%)94.7%
Missing150
Missing (%)88.8%
Memory size1.4 KiB
2023-12-12T13:01:48.616008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length20
Mean length19.526316
Min length18

Characters and Unicode

Total characters371
Distinct characters50
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)89.5%

Sample

1st row제주특별자치도 제주시 중앙로 616
2nd row제주특별자치도 제주시 중앙로 373
3rd row제주특별자치도 제주시 탑동로 26
4th row제주특별자치도 제주시 산천단남길 10
5th row제주특별자치도 제주시 서광로 222
ValueCountFrequency (%)
제주특별자치도 19
24.4%
제주시 19
24.4%
산천단남길 2
 
2.6%
10 2
 
2.6%
중앙로 2
 
2.6%
동광로 2
 
2.6%
서광로 2
 
2.6%
65 1
 
1.3%
91 1
 
1.3%
연신로 1
 
1.3%
Other values (27) 27
34.6%
2023-12-12T13:01:48.974486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
18.1%
제 38
 
10.2%
주 38
 
10.2%
도 20
 
5.4%
특 19
 
5.1%
별 19
 
5.1%
자 19
 
5.1%
치 19
 
5.1%
시 19
 
5.1%
로 15
 
4.0%
Other values (40) 98
26.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
69.8%
Space Separator 67
 
18.1%
Decimal Number 45
 
12.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 38
14.7%
주 38
14.7%
도 20
7.7%
특 19
 
7.3%
별 19
 
7.3%
자 19
 
7.3%
치 19
 
7.3%
시 19
 
7.3%
로 15
 
5.8%
길 4
 
1.5%
Other values (29) 49
18.9%
Decimal Number
ValueCountFrequency (%)
1 8
17.8%
6 7
15.6%
2 7
15.6%
3 7
15.6%
0 5
11.1%
5 4
8.9%
9 4
8.9%
7 1
 
2.2%
4 1
 
2.2%
8 1
 
2.2%
Space Separator
ValueCountFrequency (%)
67
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
69.8%
Common 112
30.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 38
14.7%
주 38
14.7%
도 20
7.7%
특 19
 
7.3%
별 19
 
7.3%
자 19
 
7.3%
치 19
 
7.3%
시 19
 
7.3%
로 15
 
5.8%
길 4
 
1.5%
Other values (29) 49
18.9%
Common
ValueCountFrequency (%)
67
59.8%
1 8
 
7.1%
6 7
 
6.2%
2 7
 
6.2%
3 7
 
6.2%
0 5
 
4.5%
5 4
 
3.6%
9 4
 
3.6%
7 1
 
0.9%
4 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 259
69.8%
ASCII 112
30.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
59.8%
1 8
 
7.1%
6 7
 
6.2%
2 7
 
6.2%
3 7
 
6.2%
0 5
 
4.5%
5 4
 
3.6%
9 4
 
3.6%
7 1
 
0.9%
4 1
 
0.9%
Hangul
ValueCountFrequency (%)
제 38
14.7%
주 38
14.7%
도 20
7.7%
특 19
 
7.3%
별 19
 
7.3%
자 19
 
7.3%
치 19
 
7.3%
시 19
 
7.3%
로 15
 
5.8%
길 4
 
1.5%
Other values (29) 49
18.9%

전화번호
Text

MISSING 

Distinct19
Distinct (%)100.0%
Missing150
Missing (%)88.8%
Memory size1.4 KiB
2023-12-12T13:01:49.184081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.052632
Min length12

Characters and Unicode

Total characters229
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)100.0%

Sample

1st row064-722-0054
2nd row064-1899-6265
3rd row064-754-1023
4th row064-720-2222
5th row064-759-5080
ValueCountFrequency (%)
064-1899-6265 1
 
5.3%
064-752-0022 1
 
5.3%
064-717-1075 1
 
5.3%
064-750-0000 1
 
5.3%
064-740-5000 1
 
5.3%
064-750-9000 1
 
5.3%
064-786-7000 1
 
5.3%
064-726-7900 1
 
5.3%
064-747-1410 1
 
5.3%
064-722-0054 1
 
5.3%
Other values (9) 9
47.4%
2023-12-12T13:01:49.593762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 59
25.8%
- 38
16.6%
6 26
11.4%
4 25
10.9%
7 24
10.5%
2 20
 
8.7%
5 12
 
5.2%
1 9
 
3.9%
9 9
 
3.9%
8 4
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 191
83.4%
Dash Punctuation 38
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59
30.9%
6 26
13.6%
4 25
13.1%
7 24
12.6%
2 20
 
10.5%
5 12
 
6.3%
1 9
 
4.7%
9 9
 
4.7%
8 4
 
2.1%
3 3
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 229
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59
25.8%
- 38
16.6%
6 26
11.4%
4 25
10.9%
7 24
10.5%
2 20
 
8.7%
5 12
 
5.2%
1 9
 
3.9%
9 9
 
3.9%
8 4
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 229
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59
25.8%
- 38
16.6%
6 26
11.4%
4 25
10.9%
7 24
10.5%
2 20
 
8.7%
5 12
 
5.2%
1 9
 
3.9%
9 9
 
3.9%
8 4
 
1.7%

데이터기준일자
Categorical

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
<NA>
150 
2021-09-07
19 

Length

Max length10
Median length4
Mean length4.6745562
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-09-07
2nd row2021-09-07
3rd row2021-09-07
4th row2021-09-07
5th row2021-09-07

Common Values

ValueCountFrequency (%)
<NA> 150
88.8%
2021-09-07 19
 
11.2%

Length

2023-12-12T13:01:49.755105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:01:49.896655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 150
88.8%
2021-09-07 19
 
11.2%

Correlations

2023-12-12T13:01:49.982931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의료기관명주소전화번호
의료기관명1.0001.0001.000
주소1.0001.0001.000
전화번호1.0001.0001.000

Missing values

2023-12-12T13:01:47.584702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:01:47.673158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-12T13:01:47.812259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

의료기관명주소전화번호데이터기준일자
0제주선한병원제주특별자치도 제주시 중앙로 616064-722-00542021-09-07
1제주우리병원제주특별자치도 제주시 중앙로 373064-1899-62652021-09-07
2탑동병원제주특별자치도 제주시 탑동로 26064-754-10232021-09-07
3제주특별자치도제주의료원제주특별자치도 제주시 산천단남길 10064-720-22222021-09-07
4지오요양병원제주특별자치도 제주시 서광로 222064-759-50802021-09-07
5아라요양병원제주특별자치도 제주시 한북로 309064-729-60002021-09-07
6제주의료원 부속 요양병원제주특별자치도 제주시 산천단남길 10064-720-22042021-09-07
7제주제일요양병원제주특별자치도 제주시 연삼로 16064-711-22102021-09-07
8제주사랑요양병원제주특별자치도 제주시 조천읍 신조로 203064-783-73502021-09-07
9토마토요양병원제주특별자치도 제주시 동광로 42064-729-50002021-09-07
의료기관명주소전화번호데이터기준일자
159<NA><NA><NA><NA>
160<NA><NA><NA><NA>
161<NA><NA><NA><NA>
162<NA><NA><NA><NA>
163<NA><NA><NA><NA>
164<NA><NA><NA><NA>
165<NA><NA><NA><NA>
166<NA><NA><NA><NA>
167<NA><NA><NA><NA>
168<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

의료기관명주소전화번호데이터기준일자# duplicates
0<NA><NA><NA><NA>150